class_docstr: Feature loader that load external features store in certain format. 
  Currently support pickle, npy and npz format.
docstr: Load samples from file_path and convert it to be of type float32
  file_path (str) is the path of the file that stores feature/sample.
docstr_tok: ["Load","samples","from","file_path","and","convert","it","to","be","of","type","float32","file_path","(","str",")","is","the","path","of","the","file","that","stores","feature\/sample","."]
code: def load_feature_from_file(self, file_path: str):
      """Load samples from file_path and convert it to be of type float32
      file_path (str) is the path of the file that stores feature/sample.
      """
  
      if file_path.endswith(".pt") or file_path.endswith(".pth"):
          samples = (
              torch.load(file_path, map_location="cpu").float().numpy()
          )
          return samples
      else:
          # load pickle/npy/npz file
          samples = np.load(file_path, allow_pickle=True)
          return self._convert_samples_to_float32(samples)
  
file_path: NeMo/nemo/collections/asr/parts/preprocessing/feature_loader.py
imports: from typing import Optional
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the ExternalFeatureLoader obj's underlying member method code with example usage: obj.load_feature_from_file(file_path) and returns: samples, self